生成式AI赋能高校思想政治教育的场景重构与实践路径

The Reconstruction of Scenarios and Practical Paths for Generative AI Empowering Ideological and Political Education in Universities

摘要: 生成式AI凭借生成性、交互性与个性化的技术优势，为高校思想政治教育突破传统育人模式瓶颈提供了创新路径。本文通过剖析生成式AI在思想政治教育内容供给、个性化育人及互动体验等方面的核心价值，梳理其在技术适配、内容质量及教师能力层面存在的现实问题。在此基础上，从课堂教学、自主学习、实践育人、思想引导四大维度探索场景重构路径，并针对性提出技术支撑、内容规范、能力提升与机制保障的实践策略，旨在为推动生成式AI与高校思想政治教育深度融合、提升育人实效提供参考。

Abstract: Generative AI, with its advantages in creativity, interactivity, and personalization, offers an innovative path for higher education institutions to break through the bottlenecks of traditional ideological and political education models. This paper analyzes the core value of generative AI in aspects such as supply of ideological and political education content, personalized education, and interactive experience, and reviews the practical issues in technology adaptation, content quality, and teacher capabilities. On this basis, it explores scene reconstruction paths from four dimensions: classroom teaching, autonomous learning, practical education, and ideological guidance, and proposes targeted practical strategies including technical support, content standards, capacity building, and mechanism guarantees. The aim is to provide a reference for promoting the deep integration of generative AI and ideological and political education in higher education and improving the effectiveness of education.